Face averages and multiple images in a live matching task
Face averages and multiple images in a live matching task
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DOI：
10.1111/bjop.12388
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发表时间：
2020-02-01
影响因子：
4
通讯作者：
Kramer, Robin S. S.
中科院分区：
文献类型：
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作者：
Ritchie, Kay L.;Mireku, Michael O.;Kramer, Robin S. S.
我们从以前的研究中知道，不熟悉的人脸匹配（确定两个同时呈现的图像是否显示同一个人）非常容易出错。在实验室环境中的少量研究表明，使用多个图像或人脸平均值，而不是单个图像，可以提高人脸匹配性能。在这里，我们在两个独立的实时匹配任务中使用四图像阵列和面部平均值测试了1，999名参与者。将单个图像与真人匹配会导致大量错误（两个实验的准确率均为79.9%），而多个图像（82.4%的准确率）和面部平均值（76.9%的准确率）都没有提高性能。这些结果是很重要的，当考虑可能的改变，可以作出照片ID。虽然多个图像和面部平均值在最近的实验室研究中的性能产生了可衡量的改善，他们不产生效益，在现实世界中的生活人脸匹配的情况下。
We know from previous research that unfamiliar face matching (determining whether two simultaneously presented images show the same person or not) is very error-prone. A small number of studies in laboratory settings have shown that the use of multiple images or a face average, rather than a single image, can improve face matching performance. Here, we tested 1,999 participants using four-image arrays and face averages in two separate live matching tasks. Matching a single image to a live person resulted in numerous errors (79.9% accuracy across both experiments), and neither multiple images (82.4% accuracy) nor face averages (76.9% accuracy) improved performance. These results are important when considering possible alterations which could be made to photo-ID. Although multiple images and face averages have produced measurable improvements in performance in recent laboratory studies, they do not produce benefits in a real-world live face matching context.